Kolmogorov complexity and its applications

我对开发一个引人注目的大数据理论很感兴趣。这种理论将取决于柯尔莫哥洛夫复杂度和信息距离。Kolmogorov复杂度定义在一个对象上。信息距离[C]。张晓明，李晓明，张晓明，张晓明。信息距离的概念与方法[j] .信息学报，2016,44(4):555 - 557。这个概念可以推广到许多对象。一般来说，使用这样的理论可以最优地近似直观的“语义距离”概念或两个数据块的接近度。这个理论的关键是压缩数据。许多压缩数据的方法将被研究，包括错误编码，聚类，特别是深度神经网络。深度神经网络可以看作是一种压缩数据，尤其是大数据的方法。以下短期目标与上述研究主题一致：

I am interested in developing a compelling theory of big data. Such a theory will depend on Kolmogorov complexity and information distance. Kolmogorov complexity is defined on one object. Information distance [C. Bennett, P. Gacs, M. Li, P. Vitanyi, W. Zurek, Information distance, IEEE Tran-IT, 44:4(1998)] is defined on two objects. This concept can be generalized to many objects. Using such a theory it is possible to optimally approximate the intuitive concept of "semantic distance" or closeness of two piece of data, in general. The key to this theory is to compress the data. Many ways of compressing data will be studied, including error encoding, clustering, and especially deep neural networks. Deep neural networks can be considered as ways of compressing data, especially big data. The following short-term goals are in tune with the above main theme of this research: